Causal mediation analysis with double machine learning

Summary This paper combines causal mediation analysis with double machine learning for a data-driven control of observed confounders in a high-dimensional setting. The average indirect effect of a binary treatment and the unmediated direct effect are estimated based on efficient score functions, whi...

Full description

Saved in:
Bibliographic Details
Published in:The econometrics journal 2022-05, Vol.25 (2), p.277-300
Main Authors: Farbmacher, Helmut, Huber, Martin, Lafférs, Lukáš, Langen, Henrika, Spindler, Martin
Format: Article
Language:English
Subjects:
Health insurance
Insurance coverage
Machine learning
Simulation
Treatment methods
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Summary This paper combines causal mediation analysis with double machine learning for a data-driven control of observed confounders in a high-dimensional setting. The average indirect effect of a binary treatment and the unmediated direct effect are estimated based on efficient score functions, which are robust with respect to misspecifications of the outcome, mediator, and treatment models. This property is key for selecting these models by double machine learning, which is combined with data splitting to prevent overfitting. We demonstrate that the effect estimators are asymptotically normal and $n^{-1/2}$-consistent under specific regularity conditions and investigate the finite sample properties of the suggested methods in a simulation study when considering lasso as machine learner. We also provide an empirical application to the US National Longitudinal Survey of Youth, assessing the indirect effect of health insurance coverage on general health operating via routine checkups as mediator, as well as the direct effect.
ISSN:1368-4221
1368-423X
DOI:10.1093/ectj/utac003